Mahalingaiah, Shruthi

Predicting polycystic ovary syndrome with machine learning algorithms from electronic health records

Zad, Z., Jiang, V. S., Wolf, A. T., Wang, T., Cheng, J. J., Paschalidis, I. C., & Mahalingaiah, S. (2024) · Frontiers in Endocrinology

Ovarian function and disordersArtificial Intelligence in Healthcare

Cited by 97

Air pollution exposure and risk of spontaneous abortion in the Nurses’ Health Study II

Gaskins, A. J., Hart, J. E., Chavarro, J. E., Missmer, S. A., Rich-Edwards, J. W., Laden, F., & Mahalingaiah, S. (2019) · Human Reproduction

Air Quality and Health ImpactsEnvironmental Justice and Health Disparities

Cited by 70

Inositol for Polycystic Ovary Syndrome: A Systematic Review and Meta-analysis to Inform the 2023 Update of the International Evidence-based PCOS Guidelines

Fitz, V., Graca, S., Mahalingaiah, S., Liu, J., Lai, L., Butt, A., Armour, M., Rao, V., Naidoo, D., Maunder, A., et al. (2024) · The Journal of Clinical Endocrinology & Metabolism

Ovarian function and disordersDiabetes, Cardiovascular Risks, and Lipoproteins

Cited by 70

Prenatal Exposure to Endocrine Disruptors: A Developmental Etiology for Polycystic Ovary Syndrome

Hewlett, M., Chow, E., Aschengrau, A., & Mahalingaiah, S. (2017) · Reproductive Sciences

Ovarian function and disordersEffects and risks of endocrine disrupting chemicals

Cited by 61

Glucagon-like peptide-1 receptor agonists and safety in the preconception period

Minis, E., Stanford, F. C., & Mahalingaiah, S. (2023) · Current Opinion in Endocrinology, Diabetes & Obesity

Pregnancy and Medication ImpactDiabetes Treatment and Management

Cited by 34

Abnormal uterine bleeding patterns determined through menstrual tracking among participants in the Apple Women’s Health Study

Zhang, C. Y., Li, H., Zhang, S., Suharwardy, S., Chaturvedi, U., Fischer-Colbrie, T., Maratta, L. A., Onnela, J.-P., Coull, B. A., Hauser, R., et al. (2023) · American Journal of Obstetrics and Gynecology

Menstrual Health and DisordersMobile Health and mHealth Applications

Cited by 25

Identifying Women at Risk for Polycystic Ovary Syndrome Using a Mobile Health App: Virtual Tool Functionality Assessment

Rodriguez, E. M., Thomas, D., Druet, A., Vlajic-Wheeler, M., Lane, K. J., & Mahalingaiah, S. (2020) · JMIR Formative Research

Mobile Health and mHealth ApplicationsOvarian function and disorders

Cited by 18